public List <NeuralNetwork> DoGeneration(List <float> fitnesses, bool complexify)
{
ResetAndKill();
for (int i = 0; i < genomes.Count; i++)
{
genomes[i].SetFitness(fitnesses[i]);
}
SortAndRecord();
SpeciateAndCaluclateSpawnLevels();
newPopulation.Clear();
//newPopulation.Add(Genome.RecycledGenome(genomes[0]));
Genome offSpring = null;
float bestFitness = -10000;
int bestSpeciesIndex = -1;
int storeNumber = Mathf.FloorToInt(genomes.Count * GA_Parameters.savePercentage / 100);
if (storeNumber == 0)
{
storeNumber = 1;
}
oldGenomes.Add(new List <Genome>());
for (int i = 0; i < storeNumber; i++)
{
oldGenomes[oldGenomes.Count - 1].Add(new Genome(genomes[i]));
}
if (complexify)
{
Complexify(bestSpeciesIndex);
}
else
{
Simplify(bestSpeciesIndex);
}
Genome.AddToRecycledGenomes(genomes);
genomes = newPopulation;
newNetworks.Clear();
for (int i = 0; i < genomes.Count; i++)
{
bool leader = false;
bool bestOfAll = false;
for (int j = 0; j < species.Count; j++)
{
if (i == 0)
{
bestOfAll = true;
break;
}
else if (genomes[i].GetID() == species[j].leader.GetID())
{
leader = true;
break;
}
}
NeuralNetwork network = genomes[i].CreateNetwork(bestOfAll, leader);
newNetworks.Add(network);
}
currentGeneration++;
return(newNetworks);
}